A TILLING resource for functional genomics in <i>Arabidopsis thaliana</i> accession C24
Bibliographic record
Abstract
TILLING (Targeting Induced Local Lesions IN Genomes) is a reverse genetic method that can be employed to generate allelic series of induced mutations in targeted genes for functional analyses. To date, TILLING resources in Arabidopsis thaliana are only available in accessions Columbia and Landsberg erecta. Here, we extended the Arabidopsis TILLING resources by developing a new population of ethyl methanesulfonate (EMS)-induced mutant lines in another commonly used A. thaliana accession C24. A permanent collection of 3,509 independent EMS mutagenized M2 lines was developed in A. thaliana accession C24, and designated C24TILL. Using the TILLING method to search C24TILL for mutations in four selected genes identified a total of 73 mutations, comprising 69.6% missense, 29.0% sense, and 1.4% nonsense mutations. Consistent with the propensity of EMS to induce guanine alkylation, 98.4% of the observed mutations were G/C to A/T transitions. Based on the mutations identified in the four target genes, the overall mutation density in the C24TILL collection was estimated to be 1/345 kb. TILLING the DUO POLLEN 1 (DUO1) gene from the C24TILL collection identified a truncation mutation leading to a deficiency in sperm cell differentiation. Taken together, a new TILLING resource, the C24TILL collection, was generated for A. thaliana accession C24. The C24TILL collection provides an allelic series of induced point mutations that will serve as a useful alternative reverse genetic resource for functional genetic studies in A. thaliana.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.068 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".